Preparation of a digital image with subsequent edge detection

ABSTRACT

For object recognition, an image is segmented into areas of similar homogeneity at a coarse scale, which are then interpreted as surfaces. Information from different spatial scales and different image features is simultaneously evaluated by exploiting statistical dependencies on their joint appearance. Thereby, the local standard deviation of specific gray levels in the close environment of an observed pixel serves as a measure for local image homogeneity that is used to get an estimate of dominant global object contours. This information is then used to mask the original image. Thus, a fine-detailed edge detection is only applied to those parts of an image where global contours exist. After that, said edges are subject to an orientation detection. Moreover, noise and small details can be suppressed, thereby contributing to the robustness of object recognition.

RELATED APPLICATIONS

[0001] This application is related to and claims priority from EuropeanPatent Applications No. 02 012 736.1 filed on Jun. 7, 2002 and 02 014189.1 filed Jun. 25, 2002 by Marc-Oliver Gewaltig, Edgar Körner, andUrsula Körner and titled “Preparation of a Digital Image with SubsequentEdge Detection”.

FIELD OF THE INVENTION

[0002] The invention relates to the field of object recognition, moreprecisely, to a contour and surface detection technique in an objectrecognition system for digitized images. The invention canadvantageously be used for the segmentation of prominent objects, and toan edge detection algorithm using oriented line segments (edges) torecognize larger objects.

BACKGROUND OF THE INVENTION

[0003] Pattern and object recognition systems that are capable ofperforming an automated scene analysis and/or object identification canbe used for a variety of tasks.

[0004] In order to recognize objects in an image, it is necessary tofirst separate those parts of the image which belong to objects(foreground) from those parts of the image which do not (background).This process is usually referred to as “image segmentation”. Imagesegmentation is typically performed for object recognition in a digitalimage, since objects should be recognized irrespective of theirbackground. Algorithms that are capable of performing this step arecalled “segmentation algorithms”.

[0005] Most standard algorithms exploit the fact that objects areseparated from the background by a more or less well defined border.They perform the segmentation step by first decomposing the image intosmall “elementary features”, like oriented line segments, which are thenused to successively construct larger objects. Segmented objects are,thus, described in terms of said elementary features.

[0006] This approach has several problems. One problem is the choice ofa suitable method for extracting object borders from the intensityvalues of the digital image. This problem becomes worse if the intensitychanges between objects and background are small, or if the intensityvariations within an object are comparable to those between object andbackground. In order to overcome this problem, a number of imageenhancement techniques are used which seek to improve the visualappearance of an image in such a way that the contrast between objectsand background are amplified. Another common problem is the choice of asuitable method for compiling an object from the set of elementaryfeatures. This problem becomes even worse if the image contains morethan one object, or if the object is surrounded by a possibly largenumber of distracting objects (clutter).

[0007] Important issues related to image segmentation are choosing goodsegmentation algorithms, measuring their performance, and understandingtheir impact on the scene analysis system.

[0008] According to the state of the art, there are different solutionsto the problem of object segmentation and recognition. In order tounderstand the main idea of the underlying invention, it is necessary tobriefly describe some of their basic features.

[0009] 1. Histogram Thresholding

[0010] In “Analysis of Natural Scenes” (PhD Thesis, Carnegie Instituteof Technology, Dept. of Computer Science, Carnegie-Mellon University,Pittsburgh, Pa., 1975) by R. B. Ohlander, a thresholding technique thatcan advantageously be applied to segmenting outdoor color images isproposed. It is based on constructing color and hue histograms. Thepicture is thresholded at its most clearly separated peak. The processiterates for each segmented part of the image until no separate peaksare found in any of the histograms.

[0011] In their article “Gray-Level Image Thresholding Based on a FisherLinear Projection of a Two-Dimensional Histogram” (Pattern Recognition,vol. 30, No. 5, pp. 743-749, 1997, incorporated herein by reference),the authors L. Li, J. Gong and W. Chen propose that the use oftwo-dimensional histograms of an image is more useful for findingthresholds for segmentation rather than just using gray-levelinformation in one dimension. In 2D-histograms, the information on pointpixels as well as the local gray level average of their neighborhood isused.

[0012] 2. Edge-Based Segmentation

[0013] In the article “Neighbor Gray Levels as Features in PixelClassification” (Pattern Recognition, vol. 12, pp. 251-260, 1980,incorporated herein by reference) by N. Ahuja, A. Rosenfeld and R. M.Haralick, it is described how pixel-neighborhood elements can be usedfor image segmentation.

[0014] In the article “Extracting and Labeling Boundary Segments inNatural Scenes” (IEEE Transactions on Pattern Analysis and MachineIntelligence, vol. 2, No. 1, pp. 16-27, 1980, incorporated herein byreference) by J. M. Prager, a set of algorithms used to perform asegmentation of natural scenes via boundary analysis is disclosed. Theaim of these algorithms is to locate the boundaries of an objectcorrectly in a scene.

[0015] In “Area Segmentation of Images using Edge Points” (IEEETransactions on Pattern Recognition and Machine Intelligence, vol. 2,No. 1, pp. 8-15, 1980, incorporated herein by reference) by W. A.Perkins, an edge-based technique for an image segmentation is employed.Therein, it is shown that edge-based segmentation has not been verysuccessful due to small gaps that allow merging dissimilar regions.

[0016] A different adaptive thresholding algorithm for imagesegmentation using variational theory is proposed in the article“Adaptive Thresholding by Variational Method” (IEEE Transactions onImage Processing, vol. 2, No. 3, pp. 168-174, 1998, incorporated hereinby reference) by F. H. Y. Chan, F. K. Lam and H. Zhu.

[0017] A further approach for an image processing based on edgedetection can be found in the article “Local Orientation Coding andNeural Network Classifiers with an Application to Real-Time CarDetection and Tracking” (in: W. G. Kropatsch and H. Bischof [editors],Mustererkennung 1994, Proceedings of the 16th Symposium of the DAGM andthe 18th Workshop of the OAGM, Springer-Verlag, 1994, incorporatedherein by reference) by C. Goerick and M. Brauckmann.

[0018] Besides these approaches mentioned above, image representationsbased on Gabor functions (GFs) and/or other similar wavelets have shownto be very useful in many applications such as image coding andcompression, enhancement and restoration, or analysis of texture.Moreover, GFs are frequently used in the scope of multi-scale filteringschemes, e.g. in current models of image representation in the visualcortex as they offer a good approximation to the receptive fields ofsimple cortical cells. however, GFs are not orthogonal and, as aconsequence, the classic Gabor expansion is computationally expensive asGFs are based on unusual dual basis functions. Said reconstructionrequires the use of iterative algorithms, Artificial Neural Networks(ANNs), or the inversion of large matrices. These problems can partiallybe overcome by using a redundant, multi-scale filtering implementation.Among the many wavelet, multi-resolution pyramids and related schemesusing different basis functions (such as Gaussian derivatives, steerablefilters, etc.), those based on GFs involve several advantages:

[0019] maximization of joint localization in both spatial and frequencydomain,

[0020] flexibility because GFs can freely be tuned to a continuum ofspatial positions, frequencies and orientations, using arbitrarybandwidths,

[0021] the fact that GFs are the only biologically plausible filterswith orientation selectivity that can exactly be expressed as a sum ofonly two separable filters, and

[0022] their good performance in a large variety of applications.

[0023] For all these reasons, Gabor functions are especially suitablefor performing early processing tasks in multipurpose environments ofimage analysis and machine vision.

[0024] In the article “Entropie als Maβ des lokalen Informationsgehaltsin Bildern zur Realisierung einer Aufinerksamkeitssteuerung” (InternalReport 96-0.7, Institut fur Neuroinformatik der Ruhr-Universitat Bochum,1996, published in: Mustererkennung 1996, pp. 627-634, Springer-Verlag,Berlin/Heidelberg, 1996, incorporated herein by reference) by T. Kalinkeand W. von Seelen, an attention control system providing an imagesegmentation for a non-specific object recognition is disclosed. Basedon the information theory introduced by C. Shannon, the localinformation content of digital images is estimated. Thereby, imageentropy is used as a measure for the expected information content of animage part. In this connection, different parameters such as mask size,subsampling factor, entropy threshold and specific parameters ofmorphological operators allow both problem- and task specific imageprocessing.

[0025] In spite of many attempts to construct optimal object recognitionsystems (e.g. based on edge detection), it can be shown that the knownalgorithms often have problems in object segmentation at locations wherelines and edges are very close and/or intersect. Since conventional edgedetection algorithms are only capable of recognizing a plurality of verysmall (simply- or multiply-connected) image patches, it is impossible toresolve local ambiguities, e.g. caused by crossing lines. Consequently,the underlying recognition system is not able to distinguish betweenmany small objects on a cluttered background and one large objectconsisting of different parts that belong together. For this reason,global information about the contours of an object that has to berecognized is required. In general, these problems occur in case ofimages that contain many different objects or in case of objects on acluttered background.

[0026] Another problem is that these object recognition systems have tobe adjusted to the used image or image family. Moreover, there are stillvery few algorithms which are able to detect and classify lines andedges (events) simultaneously.

SUMMARY OF THE INVENTION

[0027] In view of the explanations mentioned above, it is the object ofthe invention to propose a technique rendering a subsequently followingedge detection process more efficient. This object is achieved by meansof the features recited in the claims.

[0028] It should be noted that in the following description, the use of“edges”, “edge-free”, or the like, has always to be understood in thesense of the “interpretation” carried out by the process according tothe present invention. These expressions are not used in their absoluteliteral meaning.

[0029] According to a first aspect of one embodiment of the presentinvention a method for the preparation of a digital image for asubsequent pattern recognition process is proposed, wherein the methodemphasizes prominent objects in the digital image. The local standarddeviation of the image is evaluated by replacing the value of each pixelof the digital image by the standard deviation value of the gray levelsof the pixels in a defined neighborhood of a pixel to thus generate amask image. The original digital image is then combined with thegenerated mask image. The weight of the mask image can have severalfeatures. For example, the weight of the mask image can be adjustablewhen combining the original image with the mask image, or it can beadjusted by combining the mask image with an adjustable weight mask.Further, the weight mask can be designed such that only a portion of theoriginal digital image is combined with the mask image.

[0030] According to another aspect of one embodiment of the invention amethod for the preparation of a digital image for a subsequent patternrecognition is proposed, the method emphasizing prominent objects in thedigital image. Thereby pixels of the digital image that belong to anedge-free surface are detected by evaluating the local standarddeviation of the image.

[0031] The local standard deviation of the image can be evaluated byreplacing the value of each pixel of the digital image by the standarddeviation value of the gray levels of the pixels in a definedneighborhood of a pixel to generate a mask image. The original digitalimage ca be combined with the mask image to generate a combined image.Finally an edge detection may be carried out on the combined image.

[0032] The step of combining the original digital image with the maskimage can be carried out in several ways. For example, in oneembodiment, it can be carried out such that the edge detection isinhibited in areas of the digital image which are evaluated as belongingto a surface. In an alternative embodiment, the step of combining theoriginal digital image with the mask image can be carried out such thatthe edge detection is inhibited in areas of the digital image that areevaluated as not belonging to a surface. In another embodiment, the edgedetection can be carried out by means of a convolution of the combinedimage with oriented Gabor patches.

[0033] There are several advantageous variations of the presentinvention. For example, in one embodiment, the step of evaluating thelocal standard deviation can be carried out in two orthogonal directionsto thus define a standard deviation vector indicating a main orientationof the border of a surface area. In another embodiment, the values ofpixels in the main orientation can be enhanced while those of pixels offthe main orientation are suppressed. In an alternative embodiment, athreshold for the step of evaluating the local standard deviation can beadjustable. In yet another embodiment, the original digital image can below-pass filtered before the step of evaluating the local standarddeviation. Similarly, in another embodiment, a threshold functiondepending on the mean gray level of the image can be applied to thelow-pass filtered digital image and/or the mask image to enhance thecontrast.

[0034] According to still another aspect of one embodiment of thepresent invention a computer software program product implementing amethod as set forth above when running on a computing device as well asa recording medium recorded with such a computer software programproduct are proposed.

[0035] Finally the invention proposes systems presenting means forimplementing the methods as explained above.

BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Further advantages and possible applications of the underlyinginvention are depicted in the following drawings.

[0037]FIG. 1a presents a flow chart showing a functional overview of theproposed object detection algorithm according to one embodiment thepresent invention.

[0038]FIG. 1b presents a flow chart showing an alternative functionaloverview of the proposed object detection algorithm according to oneembodiment the present invention.

[0039]FIG. 2a presents a simplified block diagrams for a hardwarerealization of an object recognition system according to one embodimentthe present invention.

[0040]FIG. 2b presents a simplified block diagrams for an alternativehardware realization of an object recognition system according to oneembodiment the present invention.

DETAILED DESCRIPTION OF THE INVENTION

[0041] The invention can be used to solve several recognition and objectdetection problems, particularly, one embodiment of the presentinvention may be optimized for simultaneously evaluating informationfrom different spatial scales as well as different image features byexploiting statistical dependencies on their joint appearance, which areapplied to constructively guide a local edge detection process.

[0042] Referring to FIGS. 1a and 1 b, in one embodiment, the proposedtechnique according to the main idea of the underlying inventionprepares a digital image 102 for a subsequent pattern recognitionprocess S6, wherein the method emphasizes prominent objects in thedigital image 102.

[0043] It should be noted that in the following description, “edges”,“edge-free”, and the like, has always to be understood in the sense ofthe “interpretation” carried out by the process according to the presentinvention.

[0044] In one embodiment, the technique can be divided into the sixsteps S1 through S6 as depicted in FIG. 1a. In the following sections,these steps shall be described in detail. Sample block diagrams forhardware realization of the procedure as shown in FIG. 2a and FIG. 2bshall be explained. The meaning of the symbols designated with referencesigns in FIGS. 1 and 2 can be taken from the appended table of referencesigns.

[0045] According to one embodiment of the present invention, in a firststep S1, a digital input image A(i,j) to be recognized is submitted to aconvolution with a Gaussian kernel h(i,j) having the standard deviationσ₁. The convolution is realized by a low-pass filter 202 in FIGS. 2a, 2b and reduces noise and fine details by smoothing said input imageA(i,j). Thereby, said pixel indices i and j respectively run indirection of the x- and y-axis of said image.

[0046] The next step S2 enhances the contrast of the low-pass filteredimage. Global structures are recovered by applying a sigmoid thresholdfunction Θ₁(·), thereby yielding

C(i,j):=Θ₁ [B(i,j)] with B(i,j):=(A*h)(i,j)∀i,j,

[0047] wherein the asterisk (“*”) denotes a convolution operation, andthe sigmoid threshold function Θ₁(·) may e.g. be represented by thefollowing formula: $\begin{matrix}{{{{\Theta_{1}\left\lbrack {B\left( {i,j} \right)} \right\rbrack}:={\frac{1}{1 + {\exp \left\lbrack {{- 2} \cdot \mu_{1} \cdot \left( {\vartheta_{1} - {B\left( {i,j} \right)}} \right)} \right\rbrack}}\quad {\forall i}}},{j\quad \left( {\mu_{1} > 0} \right)}}\quad {{{{with}\quad \vartheta_{1}}:={{\langle B\rangle} = {\frac{1}{N} \cdot {\sum\limits_{({i,j})}{B\left( {i,j} \right)}}}}},}} & \quad\end{matrix}$

[0048] wherein

[0049] μ₁ is the slope factor of said threshold function Θ₁(·)

[0050] N denotes the number of pixels in the image, and

[0051] ∂₁ denotes the applied threshold value.

[0052] Said threshold ∂₁ is automatically chosen according to the meangray level

B

of the low-pass-filtered input image B(i,j). Thereby, an automatic localcalibration (auto-calibration) of this step can be achieved.

[0053] In the next step S3, pixels of the digital image belonging to thesame edge-free region are detected. A surface therefore is defined by anarea not presenting edges according to the “interpretation” carried outby the process according to the present invention. Therefore the imageis separated not by edges, but by defining adjoining (edge-free)surfaces.

[0054] The detection of the pixels belonging to the same edge-freeregion can be carried out by calculating the local standard deviationσ_(c,γ(i,j)) of the gray levels within a local (circular) neighborhoodγ(i, j) of the point (i,j) described by the observed pixel as given bythe formula $\begin{matrix}{{D\left( {i,j} \right)}:={\sigma_{c,{\gamma {({i,j})}}} = \sqrt{{Var}\left\{ C \right\}_{\gamma {({i,j})}}}}} \\{= {\sqrt{E\left\{ \left( {C - {E\left\{ C \right\}_{\gamma {({i,j})}}}} \right)^{2} \right\}_{\gamma {({i,j})}}} = \sqrt{{\langle C^{2}\rangle}_{\gamma {({i,j})}} - {\langle C\rangle}_{\gamma {({i,j})}}^{2}}}} \\{{{\sqrt{\frac{1}{P} \cdot {\sum\limits_{{({m,n})} \in {\gamma {({i,j})}}}^{\quad}\quad \left( {{C\left( {{i - m},{j - n}} \right)} - {\langle{C\left( {i,j} \right)}\rangle}_{\gamma}} \right)^{2}}}\quad {\forall i}},j}}\end{matrix}$

[0055] thereby using the result C(i,j) obtained after having applied thefirst steps (S1, S2), and${{{E\left\{ C \right\}_{\gamma {({i,j})}}} \equiv {\langle C\rangle}_{\gamma {({i,j})}}} = {\frac{1}{P} \cdot {\sum\limits_{{({m,n})} \in {\gamma {({i,j})}}}^{\quad}\quad {{C\left( {{i - m},{j - n}} \right)}{\forall i}}}}},j,$

[0056] wherein

[0057] γ(i,j) denotes a local (circular) neighborhood of the point (i,j)described by the observed pixel,

[0058]

C

_(γ(i,j)) represents the local average of the gray level of the imageC(i,j) within said environment y(i,j) around the point (i,j) afterhaving applied said noise filtering (S1), and

[0059] σ_(c,γ(i,j)) represents the local standard deviation of the graylevel of the image γ(i,j) within said environment γ(i,j), and

[0060] P is the number of pixels in said environment γ(i,j).

[0061] For this purpose, the value of each pixel is replaced by thestandard deviation value σ_(c,γ(i,j)) of the gray levels within theneighborhood y around the point (i,j) described by the respective pixel,thereby obtaining the result image D(i,j). The standard deviation σ₁(the width) of the Gaussian low-pass filter 202 as well as the size ofthe circular environment y define the spatial scale of the surfacedetection S3.

[0062] According to a particular embodiment of the present invention,the local standard deviation can be calculated in two orthogonaldirections to generate a two-dimensional standard deviation vector. Thestandard deviation vector thus indicates the dominating (main) directionof the contours of a prominent object of the image. If therefore saidmain direction is enhanced while secondary directions are suppressed,the contours of an object can be further emphasized.

[0063] In a further step S4, another sigmoid threshold function Θ₂(·)with a very steep slope _(μ2) can then be deployed to enhance contrastsby separating pixels which belong to the respective surface from thosepixels which do not belong to it:

E(i,j):=Θ₂ [D(i,j)]∀i,j

[0064] Thereby, values close or equal to zero are assigned to pixelslying within the respective surface.

[0065] The sigmoid threshold function Θ₂(·) may be represented by theformula${{\Theta_{2}\left\lbrack {D\left( {i,j} \right)} \right\rbrack}:={\frac{1}{1 + {\exp \left\lbrack {{- 2} \cdot \mu_{2} \cdot \left( {\vartheta_{2} - {D\left( {i,j} \right)}} \right)} \right\rbrack}}\quad {\forall i}}},{j\quad \left( {\mu_{2} > 0} \right)}$$\quad {{{with}\quad \vartheta_{2}}:={{\langle D\rangle} = {\frac{1}{N} \cdot {\sum\limits_{({i,j})}{D\left( {i,j} \right)}}}}}$

[0066] wherein

[0067] μ₂ is the slope factor of said threshold function Θ₂(·)

[0068] N denotes the number of pixels in the image, and

[0069] ∂₂ denotes the applied threshold value.

[0070] Said threshold ∂₂ is determined from the mean gray level

D

of the above-defined image D(i,j). Again, an automatic local calibrationof this step S4 is thereby achieved.

[0071] In a further step (S5 a or S5 b, respectively) the original imageis combined (“masked”) with the resulting image E(i,j) of the previousstep. Said masking can be executed e.g. by applying the formula

F(i,j):=[λ6·E)∘A](i,j)∀i,j(with λ≧0),

[0072] wherein the composition operator (“o”) can be replaced by apixel-wise multiplication S5 a (or addition S5 b) of the original image102 and the surface image 106, expressed by means of the operators “

” (or “⊕”, respectively).

[0073] In this step, the weighting factor λ of said surface image 106,which controls how strong the surface detection S3 shall determine thesubsequent edge detection S6, can be adjusted by combining the surfaceimage 106 with an adjustable weight mask. The weighting factor λ can bechosen uniformly for the entire image 102 or individually for a specificregion of interest.

[0074] According to a further option the weight mask can be designed insuch a way that only a portion of the original digital image 102 iscombined with the surface image 106.

[0075] In the last step S6, an edge detection is performed on the imageF(i,j). As the edge detection is inhibited in areas that are taken asbelonging to the same edge-free surface and thus limited to areascontaining edges, the efficiency of the edge detection can be enhanced.A fine-detailed edge detection S6 is typically only applied to thoseparts of an image 102 where global contours exist.

[0076] It should be noted that alternatively the edge detection couldalso be inhibited in areas containing edges to suppress prominentobjects in the image. This can be implemented by using an inversemasking in step S5 a or S5 b, respectively.

[0077] The edge detection can, for example, be performed by means of aconvolution of the contour image F(i,j) with oriented Gabor patches. Thestandard deviation σ₂ (the width) of the Gabor kernels should be assmall as possible, e.g. size 3×3 pixels. By contrast, the standarddeviation σ₁ (the width) of the Gaussian kernel should be considerablylarger, e.g. size 12×12 pixels. In this connection, it should be notedthat the size of the neighborhood γ, which is needed for the estimationof the local standard deviation σ_(c,γ(i,j)), should be chosen betweenσ₁ and σ₂.

[0078] According to one embodiment of the underlying invention, acircular patch with five pixel in diameter is used.

[0079] A program for executing the operation shown in FIGS. 1a and 1 bmay be stored in a computer readable storage medium, and this storedprogram may be executed on a computer system, so as to perform theobject detection. The computer readable storage medium may also be adevice for temporarily storing the program, such as a volatile memory(i.e. a RAM) in the computer system which functions as a server orclient for receiving the program sent via a network (e.g. the Internet)or a communication line (e.g. a telephone line). The invention may beimplemented by a hardware shown in FIGS. 2a and 2 b.

[0080] There are several advantages of the present invention. Some ofthese advantages are shown below by way of example:

[0081] The concept according to one embodiment of the present inventioncan work on different scales in parallel: at a coarse scale, the imageis segmented into areas of similar homogeneity, these areas beinginterpreted as surfaces. This effectively selects a global objectborders, independent of the local edge information.

[0082] Local ambiguities are resolved by integrating global surface andlocal edge information.

[0083] Noise and small details are suppressed which contributes to therobustness of the image recognition.

[0084] Finally, parameters of the technique according to the inventionare automatically adjusted according to the mean gray-level of theimage.

What we claim is:
 1. A method for the preparation of a digital image fora subsequent pattern recognition process, and for emphasizing prominentobjects in the digital image, the digital image comprising pixels andthe pixels having values, the method comprising the following steps:evaluating a local standard deviation of the digital image by replacingthe value of each pixel of the digital image by a standard deviationvalue of gray levels of the pixels in a defined neighborhood of thepixel to generate a mask image; and combining the digital image with themask image.
 2. The method of claim 1, wherein in the step of combiningthe image with the mask image, a weight of the mask image is adjustable.3. The method of claim 2, wherein the weight mask is designed such thatonly a portion of the digital image is combined with the mask image. 4.The method of claim 2, wherein the weight of the mask image is adjustedby combining the mask image with an adjustable weight mask.
 5. Themethod of claim 4, wherein the weight mask is designed such that only aportion of the digital image is combined with the mask image.
 6. Themethod of claim 1, wherein the step of evaluating the local standarddeviation is carried in two orthogonal directions to thus define astandard deviation vector indicating a main orientation of the contourof a surface area.
 7. The method of claim 6, wherein the values ofpixels in the main orientation are enhanced while those of pixels offthe main orientation are suppressed.
 8. The method of claim 1, wherein athreshold for the step of evaluating the local standard deviation isadjustable.
 9. The method of claim 1, wherein the digital image islow-pass filtered before the step of evaluating the local standarddeviation.
 10. The method of claim 9, wherein a threshold functiondepending on a mean gray level of the digital image is applied to thelow-pass filtered digital image to enhance the contrast.
 11. The methodof claim 1, wherein a threshold function depending on a mean gray levelof the digital image is applied to the mask image to enhance thecontrast.
 12. A computer program stored in a computer readable mediumfor performing the steps of claim
 1. 13. A method for the preparation ofa digital image for a subsequent pattern recognition, and foremphasizing prominent objects in the digital image, the methodcomprising the step of detecting pixels of the digital image whichbelong to an edge-free surface by evaluating a local standard deviationof the image.
 14. A method for the detection of edges in a digital imagehaving pixels and the pixels having values, the method comprising thefollowing steps: evaluating a local standard deviation of the image byreplacing the value of each pixel of the digital image by a standarddeviation value of gray levels of the pixels in a defined neighborhoodof the pixel to generate a mask image; combining the digital image withthe mask image to generate a combined image; and carrying out an edgedetection on the combined image.
 15. The method of claim 14, wherein thestep of combining the digital image with the mask image is designed suchthat the edge detection is inhibited in areas of the digital image thatare evaluated as belonging to a surface.
 16. The method of claim 14,wherein the edge detection is carried out by means of a convolution ofthe combined image with oriented Gabor patches.
 17. The method of claim14, wherein the step of combining the digital image with the mask imageis designed such that the edge detection is inhibited in areas of thedigital image which are evaluated as not belonging to a surface.
 18. Themethod of claim 17, wherein the edge detection is carried out by meansof a convolution of the combined image with oriented Gabor patches. 19.The method of claim 14, wherein the step of evaluating the localstandard deviation is carried in two orthogonal directions to thusdefine a standard deviation vector indicating a main orientation of thecontour of a surface area.
 20. The method of claim 19, wherein thevalues of pixels in the main orientation are enhanced while those ofpixels off the main orientation are suppressed.
 21. The method of claim14, wherein a threshold for the step of evaluating the local standarddeviation is adjustable.
 22. The method of claim 14, wherein the digitalimage is low-pass filtered before the step of evaluating the localstandard deviation.
 23. The method of claim 22, wherein a thresholdfunction depending on a mean gray level of the digital image is appliedto the low-pass filtered digital image to enhance the contrast.
 24. Themethod of claim 14, wherein a threshold function depending on a meangray level of the digital image is applied to the mask image to enhancethe contrast.
 25. A computer program stored in a computer readablemedium for performing the steps of claim
 15. 26. A system for thepreparation of a digital image for a subsequent pattern recognition, thedigital image having pixels and the pixels having values, and the systememphasizing prominent objects in the digital image and comprising: meansfor evaluating a local standard deviation of the image by replacing thevalue of each pixel of the digital image by a standard deviation valueof gray levels of the pixels in a defined neighborhood of the pixel togenerate a mask image; and means for combining the digital image withthe mask image.
 27. A system for the preparation of a digital image fora subsequent pattern recognition, the system emphasizing prominentobjects in the digital image and comprising: means for detecting pixelsof the digital image which belong to an edge-free surface by evaluatinga local standard deviation of the image.
 28. A system for the detectionof edges in a digital image having pixels and the pixels having values,the system comprising: means for evaluating a local standard deviationof the image by replacing a value of each pixel of the digital image bya standard deviation value of gray levels of pixels in a definedneighborhood of the pixel to generate a mask image; means for combiningthe digital image with the mask image to generate a combined image; andmeans for detecting edges on the combined image.
 29. A method forrecognizing an object, the method comprising the following steps:generating a low-pass filtered image by low-pass filtering an inputimage with a Gaussian kernel, the input image having pixels and thepixels having values; enhancing a contrast of the low-pass filteredimage; generating a second low-pass filtered image by detecting surfacesof the low-pass filtered image based on local variance by replacing thevalue of each pixel of the input image by the standard deviation valueof gray levels of the pixels in a defined neighborhood of thecorresponding pixel; enhancing the contrast of the second low-passfiltered image to obtain an enhanced image; combining the input imageand the enhanced image to obtain a contour image; and detecting orientededges of the contour image.
 30. An apparatus for recognizing objects,the apparatus comprising: a low-pass filter for filtering an input imagewith a Gaussian kernel; a first enhancement section for enhancing thecontrast of the low-pass filtered image; a first detection section fordetecting surfaces of the low-pass filtered image based on thelocal-variance by replacing the value of each pixel of the input imageby the standard deviation value of gray levels of the pixels in adefined neighborhood of the corresponding pixel; a second enhancementsection for enhancing the contrast of the low-pass filtered image toobtain an enhanced image; a combining section for combining the imagewith the enhanced image to obtain a contour image; and a seconddetection section for detecting oriented edges of the contour image.